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Effectively Managing Enterprise-Wide Risk

The swift movement into the age of AI is exciting, but one that presents additional risks across the enterprise that require a tighter — yet more flexible — governance structure.

Data governance is obviously not a new concept — as long as data has been collected and stored, companies have needed some level of policy and oversight for its management. However, the age of AI has ushered in data democratization, self-serve analytics, and rapid model deployment at a scale that present both more and different risks. 

For those in risk management roles, unfortunately, the process of model risk validation has not scaled as quickly as the building of models themselves. One of the advantages that data science, machine learning, and AI platforms bring is the ability to centralize data effort, allowing for model risk validation and processes to scale as well. 

For those in governance roles, the challenge is finding a balance between auditability and permission management that doesn’t stagnate the organization’s efforts to accelerate their ability to use data at scale. Of course, this requires a cost/benefit analysis, and the right balance depends largely on the industry and use case for the data (for example, marketing data is probably less sensitive and presents less risk than usage data from jet engines).

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How Dataiku Can Help

Dataiku is one of the world’s leading AI and machine learning platforms, supporting agility in organizations’ data efforts via collaborative, elastic, and responsible AI, all at enterprise scale. For those in risk or governance roles, Dataiku’s centralized, controlled, and elastic environment fuels exponential growth in the amount of data, the number of AI projects, and the number of people contributing to such projects.

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Key features include:

  • One consistent place from which to conduct model risk analysis and management thanks to a single end-to-end system housing data projects from data ingestion to deployment in production.
  • Easy communication between teams building models and those conducting model risk analysis thanks to integrated documentation and knowledge sharing.
  • Transparency and interpretability for the entire data pipeline, including models built using AutoML features.
  • Enterprise-level security controlling access to data and data projects, including support for user impersonation for full traceability and compliance.

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